Scientific Computing

丰富的科学计算生态环境

Julia is designed from the ground up to be very good at numerical and scientific computing.
This can be seen in the abundance of scientific tooling written in Julia, such as the state-of-the-art
differential equations ecosystem
(DiffEq), optimization tools
(JuMP and
Optim), iterative linear solvers
(IterativeSolvers) and many more, that can drive all your simulations.

Data Science

和你的数据交互

The Julia data ecosystem lets you load multidimensional datasets quickly, perform aggregations, joins and preprocessing operations in parallel, and save them to disk in efficient formats. You can also perform
online computations on streaming data. Whether you're looking for the convenient and familiar
DataFrames, or a new approach with
JuliaDB, Julia provides you a rich variety of tools.
The
Queryverse package acts
a meta package through which you can access these tools with Julian APIs. In addition to working with tabular data, the JuliaGraphs packages make it easy to work with combinatorial data.

Visualization

数据可视化和绘图

Data visualization has a complicated history. Plotting software makes trade-offs between features and simplicity, speed and beauty, and a static and dynamic interface. Some packages make a display and never change it, while others make updates in real-time.

Plots.jl is a visualization interface and toolset. It sits above other backends, like GR or PyPlot, connecting commands with implementation. If one backend does not support your desired features or make the right trade-offs, you can just switch to another backend with one command. No need to change your code. No need to learn a new syntax. Plots might be the last plotting package you ever learn.